CN115344074B - Lithium battery constant temperature control system based on big data - Google Patents
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 460
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 460
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to the field of constant temperature control of lithium batteries, and particularly discloses a lithium battery constant temperature control system based on big data.
Description
Technical Field
The invention relates to the field of lithium battery constant temperature control, in particular to a lithium battery constant temperature control system based on big data.
Background
A series of potential heat-release side reactions exist in the working process of the lithium battery, when the temperature of the lithium battery rises to a certain degree, the heat-release side reactions are initiated in sequence, if heat generated by the heat-release side reactions cannot be timely dissipated, the temperature of the lithium battery is further increased, and the exponential acceleration of the side reactions is caused, so that the lithium battery enters a self-heating thermal runaway state, the combustion and the explosion of the lithium battery are possibly caused, and therefore, the control method has important significance for controlling the temperature of the lithium battery.
The existing lithium battery temperature control method has some disadvantages: 1. when the tolerance temperature of the lithium battery is analyzed in the existing lithium battery temperature control method, fixed and unified standards are adopted, the influence of attenuation aging and environment temperature of the lithium battery on the tolerance temperature of the lithium battery is not considered, and then the actual tolerance temperature of the lithium battery cannot be obtained, so that the temperature of the lithium battery cannot be accurately controlled.
2. The temperature of the lithium battery is compared with a temperature threshold value by acquiring the temperature of the lithium battery in the conventional temperature control method for the lithium battery, and if the temperature of the lithium battery reaches the temperature threshold value, the temperature is reduced.
3. The conventional lithium battery temperature control method adopts constant force to cool the lithium battery, and can not dynamically adjust the cooling and radiating strength of the lithium battery according to the temperature change of the lithium battery, so that the temperature control of the lithium battery is too mechanical, and the flexibility and the intelligence are lower.
Disclosure of Invention
Aiming at the problems, the invention provides a lithium battery constant temperature control system based on big data, which realizes the function of controlling the constant temperature of a lithium battery.
The technical scheme adopted by the invention for solving the technical problems is as follows: the invention provides a lithium battery constant temperature control system based on big data, which comprises: a database: the device is used for storing high temperature resistant reference values of various types of lithium batteries and cooling regulation and control parameters of the lithium batteries.
The high temperature resistant influence parameter of the lithium battery obtains the module: the method is used for obtaining the high temperature resistant influence parameters of the lithium battery to be monitored, wherein the high temperature resistant influence parameters comprise the battery aging coefficient and the environmental temperature of the area where the battery is located.
The high-temperature-resistant influence parameter processing module of the lithium battery comprises: and processing the high-temperature early warning value to obtain the high-temperature early warning value of the lithium battery to be monitored according to the high-temperature resistant influence parameters of the lithium battery to be monitored.
Lithium battery temperature acquisition module: the method is used for acquiring the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period.
Lithium battery temperature analysis module: and analyzing to obtain the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period according to the temperature of each temperature detection point on the surface of the lithium battery to be monitored at each sampling time point in the current monitoring time period.
Lithium battery cooling judgment module: the lithium battery cooling regulation and control module is used for judging whether the lithium battery to be monitored has a cooling requirement or not according to the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period and the high-temperature early warning value of the lithium battery to be monitored, and executing the lithium battery cooling regulation and control module if the lithium battery to be monitored has the cooling requirement.
Lithium cell cooling regulation and control module: the temperature control system is used for analyzing and obtaining the cooling force proportionality coefficient of the lithium battery to be monitored according to the temperature of each sampling time point in the current monitoring time period of each temperature detection point on the surface of the lithium battery to be monitored, and regulating and controlling the air speed and the working current of the cooling fan of the lithium battery to be monitored according to the cooling force proportionality coefficient of the lithium battery to be monitored.
On the basis of the embodiment, the lithium battery cooling regulation and control parameters stored in the database comprise the air speed of the cooling fan and the working current reduction corresponding to each cooling strength grade.
On the basis of the embodiment, the high temperature resistant influence parameter of the lithium battery to be monitored is acquired in the high temperature resistant influence parameter acquisition module of the lithium battery, and the specific process is as follows: the method comprises the steps of obtaining an image of a mark part on the surface of the lithium battery to be monitored through a high-definition camera, processing the image of the mark part on the surface of the lithium battery to be monitored by utilizing an image processing technology to obtain text information corresponding to the mark part image on the surface of the lithium battery to be monitored, and obtaining the model, the nominal voltage, the nominal capacity and the rated discharge current of the lithium battery to be monitored according to the text information corresponding to the mark part image on the surface of the lithium battery to be monitored.
According to the nominal voltage and the rated discharge current of the lithium battery to be monitored, selecting electrical equipment of which the rated working voltage and the rated working current are respectively matched with the nominal voltage and the rated discharge current of the lithium battery to be monitored, marking the electrical equipment as experimental electrical equipment, connecting the lithium battery to be monitored with the experimental electrical equipment, connecting a voltmeter and an ammeter with the experimental electrical equipment, and setting the lithium battery to be monitoredThe method comprises the following steps of performing battery capacity test experiments for times, acquiring discharge voltage per unit time, discharge current per unit time and discharge duration of a lithium battery to be monitored in each battery capacity test experiment, and recording the discharge voltage per unit time, the discharge current per unit time and the discharge duration as
B represents the number of the b-th battery capacity test experiment,
substituting the discharge voltage per unit time, the discharge current per unit time and the discharge duration of the lithium battery to be monitored in each battery capacity test experiment into a formula
Obtaining the actual capacity of the lithium battery to be monitored
Wherein
And d represents the total times of the battery capacity test experiment.
Substituting the nominal capacity and the actual capacity of the lithium battery to be monitored into a formula
Obtaining the battery aging coefficient of the lithium battery to be monitored
In which
Representing a preset battery aging coefficient correction factor of the lithium battery to be monitored,
representing the nominal capacity of the lithium battery to be monitored.
Passing through temperatureThe method comprises the steps that a sensor obtains the environmental temperature of a lithium battery to be monitored within a set area range, the environmental temperature is recorded as the area environmental temperature of the lithium battery to be monitored, and the area environmental temperature of the lithium battery to be monitored is recorded as the area environmental temperature of the lithium battery to be monitored
。
On the basis of the above embodiment, the specific operation process of the battery capacity test experiment is as follows: after the experimental electrical equipment is respectively in circuit connection with the lithium battery to be monitored, the voltmeter and the ammeter, a power switch of the experimental electrical equipment is started, data acquisition time points are set in the working process of the experimental electrical equipment according to a preset equal time interval principle, discharge voltage and discharge current corresponding to the data acquisition time points are obtained through the voltmeter and the ammeter, average value calculation is respectively carried out on the discharge voltage and the discharge current corresponding to the data acquisition time points, the discharge voltage and the discharge current of the lithium battery to be monitored in unit time are obtained, when the experimental electrical equipment stops operating, the working time of the experimental electrical equipment is obtained through the timer, and the working time is recorded as the discharge time of the lithium battery to be monitored.
On the basis of the embodiment, the high-temperature early warning value of the lithium battery to be monitored is obtained by processing the high-temperature-resistant influence parameter processing module of the lithium battery, and the specific method comprises the following steps: extracting high-temperature-resistant reference values of various types of lithium batteries stored in a database, screening according to the types of the lithium batteries to be monitored to obtain the high-temperature-resistant reference values of the lithium batteries to be monitored, and recording the high-temperature-resistant reference values as the high-temperature-resistant reference values of the lithium batteries to be monitored
。
The high temperature resistance reference value of the lithium battery to be monitored
Aging factor of battery
And the ambient temperature of the area
Substituting into formula
Obtaining the high-temperature early warning value of the lithium battery to be monitored
Wherein
Representing a preset high-temperature early warning value correction factor of the lithium battery to be monitored,
respectively representing a preset high-temperature environment temperature threshold value and a preset low-temperature environment temperature threshold value.
On the basis of the above embodiment, the specific analysis process of the lithium battery temperature acquisition module is as follows: setting the duration of a monitoring time period after a lithium battery to be monitored starts working, laying all sampling time points in the current monitoring time period according to a preset equal time interval principle, laying all temperature detection points on the surface of the lithium battery to be monitored according to a preset temperature detection point laying principle, acquiring the temperature of all temperature detection points on the surface of the lithium battery to be monitored at all sampling time points in the current monitoring time period through a temperature sensor, and recording the temperature as the temperature
I denotes the number of the ith sampling time point within the current monitoring period,
j represents the number of the jth temperature detection point on the surface of the lithium battery to be monitored,
。
on the basis of the embodiment, the lithium battery temperature analysis module analyzes to obtain the next monitoring time of the lithium battery to be monitoredThe section prediction highest temperature comprises the following specific processes: the temperature of each sampling time point in the current monitoring time period at each temperature detection point on the surface of the lithium battery to be monitored
Substituting into formula
Obtaining the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
And m represents the number of the surface temperature detection points of the lithium battery to be monitored.
According to the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the time of each sampling time point in the current monitoring time period, a function fitting method is utilized to generate a temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period, the temperature rise characteristic function is marked as a target temperature rise characteristic function, and the formula is used for calculating the temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period
Obtaining the estimated time of the occurrence of the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period
Wherein
Indicating the time of the nth sampling time point within the current monitoring period,
representing the duration of the set monitoring time period, substituting the estimated time of the predicted highest temperature of the battery to be monitored in the next monitoring time period into the target temperature rise characteristic function to obtain the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, and recording the predicted highest temperature as the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period
。
On the basis of the embodiment, whether the lithium battery to be monitored has a cooling demand is judged in the lithium battery cooling judgment module, and the specific process is as follows: high-temperature early warning value of lithium battery to be monitored
And the predicted maximum temperature of the lithium battery to be monitored in the next monitoring time period
Substitution formula
Obtaining the cooling demand coefficient of the lithium battery to be monitored
Wherein
The lithium battery cooling demand coefficient correction factor is obtained by representing the preset lithium battery to be monitored, the cooling demand coefficient of the lithium battery to be monitored is compared with the preset cooling demand coefficient threshold, if the cooling demand coefficient of the lithium battery to be monitored is larger than the preset cooling demand coefficient threshold, the lithium battery to be monitored has a cooling demand, otherwise, the lithium battery to be monitored does not have a cooling demand.
On the basis of the embodiment, the cooling force proportional coefficient of the lithium battery to be monitored is obtained by analyzing in the lithium battery cooling regulation and control module, and the specific process is as follows: average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
Substitution formula
Obtaining the temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
Wherein
Representing a temperature rise rate coefficient correction factor of a preset lithium battery to be monitored in the current monitoring time period, n representing the total number of sampling time points in the current monitoring time period,
indicating that the lithium battery to be monitored is in the current monitoring time period
The average temperature of the individual sampling time points,
indicating that the lithium battery to be monitored is in the current monitoring time period
Average temperature for each sampling time point.
Comparing the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period, screening out the maximum average temperature of the lithium battery to be monitored in the current monitoring time period, and recording the maximum average temperature as the maximum average temperature
Substituting the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the maximum average temperature of the lithium battery to be monitored in the current monitoring time period into a formula
Obtaining the temperature rise amplitude coefficient of the lithium battery to be monitored in the current monitoring time period
In which
And representing the temperature rise amplitude coefficient correction factor of the preset lithium battery to be monitored in the current monitoring time period.
The temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
Amplitude coefficient of temperature rise
Substitution formula
Obtaining the cooling force proportional coefficient of the lithium battery to be monitored
Wherein
Representing a preset temperature reduction force proportional coefficient correction factor of the lithium battery to be monitored,
and respectively representing weight factors of a temperature rise rate coefficient and a temperature rise amplitude coefficient of the preset lithium battery to be monitored in the current monitoring time period.
On the basis of the embodiment, according to the cooling dynamics proportionality coefficient of waiting to monitor the lithium cell among the lithium cell cooling regulation and control module, treat the heat dissipation fan wind speed and the operating current of monitoring the lithium cell and regulate and control, concrete process is: the cooling dynamics proportionality coefficient that will wait to monitor the lithium cell compares with the cooling dynamics proportionality coefficient reference range that each predetermined cooling dynamics grade corresponds, the screening obtains the cooling dynamics grade of waiting to monitor the lithium cell, extract the heat dissipation fan wind speed and the operating current decrement that each cooling dynamics grade of storage corresponds in the database, according to the cooling dynamics grade of waiting to monitor the lithium cell, the screening obtains the heat dissipation fan wind speed and the operating current decrement of waiting to monitor the lithium cell, and then the heat dissipation fan wind speed and the operating current of waiting to monitor the lithium cell are regulated and control.
Compared with the prior art, the lithium battery constant temperature control system based on big data has the following beneficial effects: 1. according to the lithium battery constant temperature control system based on the big data, whether the lithium battery to be monitored has a cooling requirement is judged by obtaining the high-temperature early warning value of the lithium battery to be monitored and the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, the cooling force proportional coefficient of the lithium battery to be monitored is further obtained, and the air speed and the working current of a cooling fan of the lithium battery to be monitored are regulated and controlled according to the cooling force proportional coefficient of the lithium battery to be monitored, so that the reliability and the accuracy of the temperature of the lithium battery are controlled.
2. According to the lithium battery constant temperature control system based on the big data, the high-temperature early warning value of the lithium battery to be monitored is obtained through processing by obtaining the battery aging coefficient and the environmental temperature of the area where the battery to be monitored is located, the tolerance temperature of the lithium battery is analyzed by integrating the attenuation aging coefficient and the environmental temperature of the lithium battery, so that the tolerance temperature of the lithium battery is closer to the true value, and the temperature of the lithium battery can be accurately controlled.
3. According to the lithium battery constant temperature control system based on the big data, the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period is obtained, the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period is obtained through analysis, whether the lithium battery to be monitored has a cooling requirement or not is judged according to the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period and the high-temperature early warning value of the lithium battery to be monitored, the predicted temperature of the lithium battery is obtained through analysis of the temperature change trend of the lithium battery, corresponding treatment is carried out according to the predicted temperature of the lithium battery, the lithium battery can be cooled before the lithium battery enters a high-temperature state, the scientificity and the energy efficiency of temperature control of the lithium battery are greatly improved, and further damage to the lithium battery caused by high temperature is reduced.
4. According to the lithium battery constant temperature control system based on the big data, the temperature rise rate coefficient and the temperature rise amplitude coefficient of the lithium battery to be monitored in the current monitoring time period are obtained through the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period, the cooling force proportion coefficient of the lithium battery to be monitored is obtained through analysis, and the cooling heat dissipation intensity of the lithium battery is dynamically adjusted through the rate and the amplitude of the temperature rise of the lithium battery, so that the flexibility and the intelligence of temperature control of the lithium battery are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system module connection diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention provides a lithium battery constant temperature control system based on big data, which includes a database, a lithium battery high temperature resistant influence parameter acquisition module, a lithium battery high temperature resistant influence parameter processing module, a lithium battery temperature acquisition module, a lithium battery temperature analysis module, a lithium battery cooling judgment module, and a lithium battery cooling regulation module.
The lithium battery high-temperature-resistant influence parameter acquisition module is connected with the lithium battery high-temperature-resistant influence parameter processing module, the lithium battery temperature acquisition module is connected with the lithium battery temperature analysis module, the lithium battery cooling judgment module is respectively connected with the lithium battery high-temperature-resistant influence parameter processing module and the lithium battery temperature analysis module, the lithium battery cooling regulation and control module is connected with the lithium battery cooling judgment module, and the database is respectively connected with the lithium battery high-temperature-resistant influence parameter processing module and the lithium battery cooling regulation and control module.
The database is used for storing high-temperature-resistant reference values of various types of lithium batteries and cooling regulation and control parameters of the lithium batteries.
Further, the lithium battery cooling regulation and control parameters stored in the database include the cooling fan speed and the working current reduction corresponding to each cooling force level.
The high-temperature-resistant influence parameter acquisition module of the lithium battery is used for acquiring high-temperature-resistant influence parameters of the lithium battery to be monitored, wherein the high-temperature-resistant influence parameters comprise a battery aging coefficient and the ambient temperature of an area where the battery is located.
Further, the high temperature resistant influence parameter of the lithium battery to be monitored is obtained in the high temperature resistant influence parameter obtaining module of the lithium battery, and the specific process is as follows: the method comprises the steps of obtaining an image of a mark part on the surface of the lithium battery to be monitored through a high-definition camera, processing the image of the mark part on the surface of the lithium battery to be monitored by utilizing an image processing technology to obtain character information corresponding to the mark part image on the surface of the lithium battery to be monitored, and obtaining the model, the nominal voltage, the nominal capacity and the rated discharge current of the lithium battery to be monitored according to the character information corresponding to the mark part image on the surface of the lithium battery to be monitored.
Selecting electrical equipment with rated working voltage and rated working current respectively matched with the nominal voltage and the rated discharge current of the lithium battery to be monitored according to the nominal voltage and the rated discharge current of the lithium battery to be monitored, marking the electrical equipment as experimental electrical equipment, connecting the lithium battery to be monitored with the experimental electrical equipment, connecting a voltmeter and an ammeter with the experimental electrical equipment, performing battery capacity test experiments on the lithium battery to be monitored for set times, acquiring discharge voltage, discharge current and discharge duration of the lithium battery to be monitored in each battery capacity test experiment in unit time, and respectively marking the discharge voltage, the discharge current and the discharge duration as the discharge voltage, the discharge current and the discharge duration of the lithium battery to be monitored in unit time
And b represents the number of the b-th battery capacity test experiment，
Substituting discharge voltage per unit time, discharge current per unit time and discharge duration of lithium battery to be monitored in each battery capacity test experiment into a formula
Obtaining the actual capacity of the lithium battery to be monitored
Wherein
And d represents the total times of the battery capacity test experiment.
Substituting the nominal capacity and the actual capacity of the lithium battery to be monitored into a formula
Obtaining the battery aging coefficient of the lithium battery to be monitored
Wherein
Representing a preset battery aging coefficient correction factor of the lithium battery to be monitored,
representing the nominal capacity of the lithium battery to be monitored.
The ambient temperature of the lithium battery to be monitored in the set area range is obtained through the temperature sensor and is recorded as the ambient temperature of the area where the lithium battery to be monitored is located, and the ambient temperature of the area where the lithium battery to be monitored is located is recorded as the ambient temperature of the area where the lithium battery to be monitored is located
。
As a preferred scheme, the selection method of the experimental electrical equipment comprises the following steps: the rated working voltage of the experimental electrical equipment is the same as the rated voltage of the lithium battery to be monitored, the rated working current of the experimental electrical equipment is smaller than the rated discharge current of the lithium battery to be monitored, and the difference value between the rated working current of the experimental electrical equipment and the rated discharge current of the lithium battery to be monitored is within the preset current difference value allowable range.
Further, the specific operation process of the battery capacity test experiment is as follows: after the experimental electrical equipment is respectively in circuit connection with the lithium battery to be monitored, the voltmeter and the ammeter, a power switch of the experimental electrical equipment is started, data acquisition time points are set in the working process of the experimental electrical equipment according to a preset equal time interval principle, discharge voltage and discharge current corresponding to the data acquisition time points are obtained through the voltmeter and the ammeter, average value calculation is respectively carried out on the discharge voltage and the discharge current corresponding to the data acquisition time points, the discharge voltage and the discharge current of the lithium battery to be monitored in unit time are obtained, when the experimental electrical equipment stops operating, the working time of the experimental electrical equipment is obtained through the timer, and the working time is recorded as the discharge time of the lithium battery to be monitored.
The high-temperature-resistant influence parameter processing module of the lithium battery is used for processing the high-temperature-resistant influence parameter of the lithium battery to be monitored to obtain a high-temperature early warning value of the lithium battery to be monitored.
Further, the high temperature early warning value of the lithium battery to be monitored is obtained by processing in the high temperature resistant influence parameter processing module of the lithium battery, and the specific method comprises the following steps: extracting high-temperature-resistant reference values of various types of lithium batteries stored in a database, screening according to the types of the lithium batteries to be monitored to obtain the high-temperature-resistant reference values of the lithium batteries to be monitored, and recording the high-temperature-resistant reference values as the high-temperature-resistant reference values of the lithium batteries to be monitored
。
The high temperature resistance reference value of the lithium battery to be monitored
Aging factor of battery
And the ambient temperature of the area
Substitution formula
Obtaining the high-temperature early warning value of the lithium battery to be monitored
Wherein
Representing a preset high-temperature early warning value correction factor of the lithium battery to be monitored,
respectively representing a preset high-temperature environment temperature threshold value and a preset low-temperature environment temperature threshold value.
It should be noted that the high-temperature early warning value of the lithium battery to be monitored is obtained by acquiring the battery aging coefficient of the lithium battery to be monitored and the environmental temperature of the area where the battery to be monitored is located, and the high-temperature early warning value of the lithium battery to be monitored is obtained by integrating the attenuation aging coefficient of the lithium battery and the environmental temperature to analyze the tolerance temperature of the lithium battery, so that the tolerance temperature of the lithium battery is closer to the true value, and the temperature of the lithium battery can be accurately controlled.
The lithium battery temperature acquisition module is used for acquiring the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period.
Further, the specific analysis process of the lithium battery temperature acquisition module is as follows: setting the duration of a monitoring time period after a lithium battery to be monitored starts working, laying all sampling time points in the current monitoring time period according to a preset equal time interval principle, laying all temperature detection points on the surface of the lithium battery to be monitored according to a preset temperature detection point laying principle, and acquiring the temperature detection points on the surface of the lithium battery to be monitored through a temperature sensorThe temperature of each sampling time point in the current monitoring time period is recorded as
I denotes the number of the ith sampling time point in the current monitoring period,
j represents the number of the jth temperature detection point on the surface of the lithium battery to be monitored,
。
and the lithium battery temperature analysis module is used for analyzing and obtaining the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period according to the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period.
Further, the lithium battery temperature analysis module analyzes and obtains the predicted maximum temperature of the lithium battery to be monitored in the next monitoring time period, and the specific process is as follows: the temperature of each sampling time point in the current monitoring time period at each temperature detection point on the surface of the lithium battery to be monitored
Substituting into formula
Obtaining the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
And m represents the number of the surface temperature detection points of the lithium battery to be monitored.
According to the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the time of each sampling time point in the current monitoring time period, a function fitting method is utilized to generate a temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period, the temperature rise characteristic function is marked as a target temperature rise characteristic function, and the formula is used for calculating the temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period
Obtaining the estimated time of the occurrence of the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period
In which
Indicating the time of the nth sampling time point within the current monitoring period,
representing the time length of the set monitoring time period, substituting the estimated time of the predicted highest temperature of the battery to be monitored in the next monitoring time period into the target temperature rise characteristic function to obtain the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, and recording the predicted highest temperature as the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period
。
As a preferred scheme, the function fitting method is used for generating a temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period, and the specific process is as follows: and taking the time of the sampling time point as an independent variable of the temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period, and taking the temperature of the lithium battery to be monitored as a dependent variable of the temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period.
The lithium battery cooling judgment module is used for judging whether the lithium battery to be monitored has a cooling demand or not according to the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period and the high-temperature early warning value of the lithium battery to be monitored, and if the lithium battery to be monitored has the cooling demand, the lithium battery cooling regulation and control module is executed.
Further, judge in the lithium cell cooling judge module whether to wait to monitor the lithium cell and have the cooling demand, concrete process is: high-temperature early warning value of lithium battery to be monitored
And the predicted maximum temperature of the lithium battery to be monitored in the next monitoring time period
Substituting into formula
Obtaining the cooling demand coefficient of the lithium battery to be monitored
Wherein
The lithium battery cooling demand coefficient correction factor is obtained by representing the preset lithium battery to be monitored, the cooling demand coefficient of the lithium battery to be monitored is compared with the preset cooling demand coefficient threshold, if the cooling demand coefficient of the lithium battery to be monitored is larger than the preset cooling demand coefficient threshold, the lithium battery to be monitored has a cooling demand, otherwise, the lithium battery to be monitored does not have a cooling demand.
The method comprises the steps of obtaining the temperature of each temperature detection point on the surface of the lithium battery to be monitored at each sampling time point in the current monitoring time period, analyzing to obtain the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, judging whether the lithium battery to be monitored has a cooling requirement according to the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period and the high-temperature early warning value of the lithium battery to be monitored, obtaining the predicted temperature of the lithium battery by analyzing the temperature change trend of the lithium battery, performing corresponding processing according to the predicted temperature of the lithium battery, cooling the lithium battery before the lithium battery enters a high-temperature state, greatly improving the scientificity and energy efficiency of temperature control of the lithium battery, and further reducing the damage of the lithium battery caused by high temperature.
The lithium battery cooling regulation and control module is used for analyzing and obtaining the cooling force proportionality coefficient of the lithium battery to be monitored according to the temperature of each sampling time point in the current monitoring time period of each temperature detection point on the surface of the lithium battery to be monitored, and regulating and controlling the air speed and the working current of the cooling fan of the lithium battery to be monitored according to the cooling force proportionality coefficient of the lithium battery to be monitored.
Further, the cooling force proportional coefficient of the lithium battery to be monitored is obtained by analyzing in the lithium battery cooling regulation and control module, and the specific process is as follows: average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
Substituting into formula
Obtaining the temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
In which
Representing a temperature rise rate coefficient correction factor of a preset lithium battery to be monitored in the current monitoring time period, n representing the total number of sampling time points in the current monitoring time period,
indicating that the lithium battery to be monitored is in the current monitoring time period
The average temperature of the individual sampling time points,
indicating that the lithium battery to be monitored is in the current monitoring time period
Average temperature for each sampling time point.
Comparing the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period, and screening out the maximum average temperature of the lithium battery to be monitored in the current monitoring time periodDegree, it is denoted as
Substituting the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the maximum average temperature of the lithium battery to be monitored in the current monitoring time period into a formula
Obtaining the temperature rise amplitude coefficient of the lithium battery to be monitored in the current monitoring time period
Wherein
And representing the temperature rise amplitude coefficient correction factor of the preset lithium battery to be monitored in the current monitoring time period.
The temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
Amplitude coefficient of temperature rise
Substitution formula
Obtaining the cooling force proportional coefficient of the lithium battery to be monitored
In which
Representing a preset temperature reduction force proportional coefficient correction factor of the lithium battery to be monitored,
respectively representing the temperature rise rate system of the preset lithium battery to be monitored in the current monitoring time periodNumber and weight factor of temperature rise amplitude coefficient.
It should be noted that, in the present invention, the temperature rise rate coefficient and the temperature rise amplitude coefficient of the lithium battery to be monitored in the current monitoring period are obtained through the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring period, the cooling force proportional coefficient of the lithium battery to be monitored is obtained through analysis, and the cooling heat dissipation strength of the lithium battery is dynamically adjusted through the rate and the amplitude of the temperature rise of the lithium battery, so that the flexibility and the intelligence of the temperature control of the lithium battery are both improved.
Furthermore, according to the cooling dynamics proportionality coefficient of waiting to monitor the lithium cell among the lithium cell cooling regulation and control module, treat the heat dissipation fan wind speed and the operating current of monitoring the lithium cell and regulate and control, concrete process is: the cooling force proportionality coefficient of the lithium battery to be monitored is compared with a cooling force proportionality coefficient reference range corresponding to each preset cooling force grade, the cooling force grades of the lithium battery to be monitored are obtained through screening, the cooling fan wind speed and the working current reduction amount corresponding to each cooling force grade stored in the database are extracted, according to the cooling force grades of the lithium battery to be monitored, the cooling fan wind speed and the working current reduction amount of the lithium battery to be monitored are obtained through screening, and then the cooling fan wind speed and the working current of the lithium battery to be monitored are regulated and controlled.
It should be noted that the cooling force proportional coefficient of the lithium battery to be monitored is used for regulating and controlling the air speed and the working current of the cooling fan of the lithium battery to be monitored, the working current is regulated to cool the interior of the lithium battery, the air speed of the cooling fan is regulated simultaneously to cool the exterior of the lithium battery, and various cooling and heat dissipation modes are adopted to cool the lithium battery, so that the actual cooling effect of the lithium battery is enhanced, and the use safety of the lithium battery is guaranteed.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (5)
1. The utility model provides a lithium cell constant temperature control system based on big data which characterized in that includes:
a database: the device is used for storing high-temperature-resistant reference values and cooling regulation and control parameters of lithium batteries of various models;
the lithium battery high temperature resistance influence parameter acquisition module: the method comprises the steps of obtaining high temperature resistant influence parameters of the lithium battery to be monitored, wherein the high temperature resistant influence parameters comprise a battery aging coefficient and the environmental temperature of the area where the battery is located;
the high-temperature-resistant influence parameter processing module of the lithium battery comprises: the high-temperature early warning device is used for processing to obtain a high-temperature early warning value of the lithium battery to be monitored according to the high-temperature resistant influence parameters of the lithium battery to be monitored;
lithium battery temperature acquisition module: the temperature monitoring device is used for acquiring the temperature of each temperature detection point on the surface of the lithium battery to be monitored at each sampling time point in the current monitoring time period;
lithium battery temperature analysis module: the system is used for analyzing and obtaining the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period according to the temperature of each sampling time point of each temperature detection point on the surface of the lithium battery to be monitored in the current monitoring time period;
lithium battery cooling judgment module: the lithium battery temperature-reducing regulation and control module is used for judging whether the lithium battery to be monitored has a temperature-reducing requirement or not according to the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period and the high-temperature early warning value of the lithium battery to be monitored, and executing the lithium battery temperature-reducing regulation and control module if the lithium battery to be monitored has the temperature-reducing requirement;
lithium cell cooling regulation and control module: the monitoring system is used for analyzing and obtaining a cooling force proportional coefficient of the lithium battery to be monitored according to the temperature of each temperature detection point on the surface of the lithium battery to be monitored at each sampling time point in the current monitoring time period, and regulating and controlling the air speed and the working current of a cooling fan of the lithium battery to be monitored according to the cooling force proportional coefficient of the lithium battery to be monitored;
the lithium battery high temperature resistant influence parameter acquisition module acquires the high temperature resistant influence parameter of the lithium battery to be monitored, and the specific process is as follows:
acquiring an image of a surface identification part of a lithium battery to be monitored by a high-definition camera, processing the image of the surface identification part of the lithium battery to be monitored by using an image processing technology to obtain character information corresponding to the surface identification part image of the lithium battery to be monitored, and obtaining the model, the nominal voltage, the nominal capacity and the rated discharge current of the lithium battery to be monitored according to the character information corresponding to the surface identification part image of the lithium battery to be monitored;
according to the nominal voltage and the rated discharge current of the lithium battery to be monitored, selecting electrical equipment of which the rated working voltage and the rated working current are respectively matched with the nominal voltage and the rated discharge current of the lithium battery to be monitored, recording the electrical equipment as experimental electrical equipment, connecting the lithium battery to be monitored with the experimental electrical equipment, connecting a voltmeter and an ammeter with the experimental electrical equipment, performing battery capacity test experiments of set times on the lithium battery to be monitored, acquiring the discharge voltage, discharge current and discharge duration of the lithium battery to be monitored in each battery capacity test experiment, and respectively recording the discharge voltage, discharge current and discharge duration as the discharge voltage, discharge current and discharge duration of the lithium battery to be monitored in each battery capacity test experiment
B represents the number of the b-th battery capacity test experiment,
substituting the discharge voltage per unit time, the discharge current per unit time and the discharge duration of the lithium battery to be monitored in each battery capacity test experiment into a formula
Obtaining the actual capacity of the lithium battery to be monitored
Wherein
Representing a preset actual capacity correction factor of the lithium battery to be monitored, and d representing the total times of battery capacity test experiments;
substituting the nominal capacity and the actual capacity of the lithium battery to be monitored into a formula
Obtaining the battery aging coefficient of the lithium battery to be monitored
Wherein
Representing a preset battery aging coefficient correction factor of the lithium battery to be monitored,
representing the nominal capacity of the lithium battery to be monitored;
the ambient temperature of the lithium battery to be monitored in the set area range is obtained through the temperature sensor and is recorded as the ambient temperature of the area where the lithium battery to be monitored is located, and the ambient temperature of the area where the lithium battery to be monitored is located is recorded as the ambient temperature of the area where the lithium battery to be monitored is located
；
The high-temperature early warning value of the lithium battery to be monitored is obtained by processing in the high-temperature-resistant influence parameter processing module of the lithium battery, and the specific method comprises the following steps:
extracting high-temperature-resistant reference values of various types of lithium batteries stored in a database, screening according to the types of the lithium batteries to be monitored to obtain the high-temperature-resistant reference values of the lithium batteries to be monitored, and recording the high-temperature-resistant reference values as the high-temperature-resistant reference values of the lithium batteries to be monitored
；
The high temperature resistance reference value of the lithium battery to be monitored
Aging factor of battery
And the ambient temperature of the area
Substitution formula
Obtaining the high-temperature early warning value of the lithium battery to be monitored
Wherein
Representing a preset high-temperature early warning value correction factor of the lithium battery to be monitored,
respectively representing a preset high-temperature environment temperature threshold and a preset low-temperature environment temperature threshold;
the specific analysis process of the lithium battery temperature acquisition module is as follows:
setting the duration of a monitoring time period after a lithium battery to be monitored starts working, laying all sampling time points in the current monitoring time period according to a preset equal time interval principle, laying all temperature detection points on the surface of the lithium battery to be monitored according to a preset temperature detection point laying principle, acquiring the temperature of all temperature detection points on the surface of the lithium battery to be monitored at all sampling time points in the current monitoring time period through a temperature sensor, and recording the temperature as the temperature
I denotes the number of the ith sampling time point in the current monitoring period,
j represents the number of the jth temperature detection point on the surface of the lithium battery to be monitored,
；
the lithium battery temperature analysis module analyzes and obtains the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, and the specific process is as follows:
the temperature of each sampling time point in the current monitoring time period of each temperature detection point on the surface of the lithium battery to be monitored
Substitution formula
Obtaining the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
Wherein m represents the number of the surface temperature detection points of the lithium battery to be monitored;
according to the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the time of each sampling time point in the current monitoring time period, a function fitting method is utilized to generate a temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period, the temperature rise characteristic function is marked as a target temperature rise characteristic function, and the formula is used for calculating the temperature rise characteristic function of the lithium battery to be monitored in the current monitoring time period
Obtaining the estimated time of the occurrence of the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period
In which
Indicating the time of the nth sampling time point within the current monitoring period,
the duration of the set monitoring time period is shown, and the battery to be monitoredSubstituting the predicted time for predicting the occurrence of the highest temperature in the next monitoring time period into the target temperature rise characteristic function to obtain the predicted highest temperature of the lithium battery to be monitored in the next monitoring time period, and recording the predicted highest temperature as the predicted highest temperature
；
The cooling force proportional coefficient of the lithium battery to be monitored is obtained by analyzing in the lithium battery cooling regulation and control module, and the specific process is as follows:
average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period
Substitution formula
Obtaining the temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
Wherein
Representing a temperature rise rate coefficient correction factor of a preset lithium battery to be monitored in the current monitoring time period, n representing the total number of sampling time points in the current monitoring time period,
indicating that the lithium battery to be monitored is in the current monitoring time period
The average temperature at each sampling time point,
indicating that the lithium battery to be monitored is in the current monitoring time period
Average temperature of each sampling time point;
comparing the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period, screening out the maximum average temperature of the lithium battery to be monitored in the current monitoring time period, and recording the maximum average temperature as the maximum average temperature
Substituting the average temperature of each sampling time point of the lithium battery to be monitored in the current monitoring time period and the maximum average temperature of the lithium battery to be monitored in the current monitoring time period into a formula
Obtaining the temperature rise amplitude coefficient of the lithium battery to be monitored in the current monitoring time period
Wherein
Representing a temperature rise amplitude coefficient correction factor of a preset lithium battery to be monitored in the current monitoring time period;
the temperature rise rate coefficient of the lithium battery to be monitored in the current monitoring time period
Amplitude coefficient of temperature rise
Substitution formula
Obtaining the cooling force proportional coefficient of the lithium battery to be monitored
Wherein
Representing a preset temperature reduction force proportional coefficient correction factor of the lithium battery to be monitored,
and respectively representing weight factors of a temperature rise rate coefficient and a temperature rise amplitude coefficient of the preset lithium battery to be monitored in the current monitoring time period.
2. The lithium battery constant temperature control system based on big data according to claim 1, characterized in that: the lithium battery cooling regulation and control parameters stored in the database comprise the air speed of a cooling fan and the working current reduction corresponding to each cooling strength grade.
3. The lithium battery constant temperature control system based on big data according to claim 1, characterized in that: the specific operation process of the battery capacity test experiment comprises the following steps:
after the experimental electrical equipment is respectively in circuit connection with the lithium battery to be monitored, the voltmeter and the ammeter, a power switch of the experimental electrical equipment is started, data acquisition time points are set in the working process of the experimental electrical equipment according to a preset equal time interval principle, discharge voltage and discharge current corresponding to the data acquisition time points are obtained through the voltmeter and the ammeter, average value calculation is respectively carried out on the discharge voltage and the discharge current corresponding to the data acquisition time points, the discharge voltage and the discharge current of the lithium battery to be monitored in unit time are obtained, when the experimental electrical equipment stops operating, the working time of the experimental electrical equipment is obtained through the timer, and the working time is recorded as the discharge time of the lithium battery to be monitored.
4. The lithium battery constant temperature control system based on big data according to claim 1, characterized in that: whether the lithium battery to be monitored has a cooling demand is judged in the lithium battery cooling judgment module, and the specific process is as follows:
high temperature early warning value of lithium battery to be monitored
And the predicted maximum temperature of the lithium battery to be monitored in the next monitoring time period
Substitution formula
Obtaining the cooling demand coefficient of the lithium battery to be monitored
In which
Express the cooling demand coefficient correction factor of the predetermined lithium cell of waiting to monitor, will wait to monitor the cooling demand coefficient of lithium cell and compare with preset cooling demand coefficient threshold value, if the cooling demand coefficient of waiting to monitor the lithium cell is greater than preset cooling demand coefficient threshold value, then wait to monitor the lithium cell and have the cooling demand, otherwise, then wait to monitor the lithium cell and do not have the cooling demand.
5. The lithium battery constant temperature control system based on big data according to claim 2, characterized in that: according to the cooling dynamics proportionality coefficient of waiting to monitor the lithium cell among the lithium cell cooling regulation and control module, treat the heat dissipation fan wind speed and the operating current who monitors the lithium cell and regulate and control, concrete process is:
the cooling force proportionality coefficient of the lithium battery to be monitored is compared with a cooling force proportionality coefficient reference range corresponding to each preset cooling force grade, the cooling force grades of the lithium battery to be monitored are obtained through screening, the cooling fan wind speed and the working current reduction amount corresponding to each cooling force grade stored in the database are extracted, according to the cooling force grades of the lithium battery to be monitored, the cooling fan wind speed and the working current reduction amount of the lithium battery to be monitored are obtained through screening, and then the cooling fan wind speed and the working current of the lithium battery to be monitored are regulated and controlled.
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| CN116449235B (en) * | 2023-06-14 | 2023-08-29 | 江苏安之技科技发展有限公司 | Method and system for processing test data of energy storage battery |
| CN116864872B (en) * | 2023-08-18 | 2024-08-16 | 深圳森能新能源科技有限公司 | Intelligent heat dissipation control method and device for realizing constant temperature of battery pack and battery pack |
| CN117435990B (en) * | 2023-10-19 | 2024-05-31 | 北华航天工业学院 | Ultrasonic rolling processing temperature detection and analysis method |
| CN117525692B (en) * | 2023-10-26 | 2024-07-05 | 苏州华骞时代新能源科技有限公司 | Control method and system of safe energy storage system |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11283681A (en) * | 1998-03-27 | 1999-10-15 | Sony Corp | Rechargeable power supply device and method for cooling the same |
| WO2016061034A1 (en) * | 2014-10-15 | 2016-04-21 | Johnson Controls Technology Company | Cooling strategy for battery systems |
| CN109962295A (en) * | 2017-12-14 | 2019-07-02 | 宝山钢铁股份有限公司 | A kind of temperature-control pressure-control formula formation of Li-ion batteries method |
| CN110148336A (en) * | 2019-03-27 | 2019-08-20 | 北京航空航天大学 | A kind of lithium ion battery charging process failure simulation method and device |
| CN110635187A (en) * | 2019-09-01 | 2019-12-31 | 南京理工大学 | Lithium battery charging method considering aging |
| CN111208431A (en) * | 2020-01-07 | 2020-05-29 | 天津市捷威动力工业有限公司 | Lithium ion battery all-weather calendar life prediction method for electric automobile |
| CN113178643A (en) * | 2021-03-16 | 2021-07-27 | 中国科学院电工研究所 | Hybrid heating method, system and equipment for low-temperature direct-current discharge of lithium ion battery |
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
| CN114572058A (en) * | 2022-03-09 | 2022-06-03 | 重庆金康赛力斯新能源汽车设计院有限公司 | Method and device for controlling temperature of battery system and electronic equipment |
| CN114725565A (en) * | 2022-03-29 | 2022-07-08 | 重庆金康赛力斯新能源汽车设计院有限公司 | High-temperature quick-charging cooling method, device, equipment and storage medium for lithium ion battery |
| EP4046859A1 (en) * | 2021-02-19 | 2022-08-24 | Transportation IP Holdings, LLC | Battery management system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9331507B2 (en) * | 2010-10-18 | 2016-05-03 | The Johns Hopkins University | Control apparatus and method for conducting fast battery charge |
| CN111505503B (en) * | 2020-04-17 | 2021-06-15 | 北京航空航天大学 | Lithium ion battery aging test method and test device based on micro mechanism |
-
2022
- 2022-10-18 CN CN202211269601.3A patent/CN115344074B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11283681A (en) * | 1998-03-27 | 1999-10-15 | Sony Corp | Rechargeable power supply device and method for cooling the same |
| WO2016061034A1 (en) * | 2014-10-15 | 2016-04-21 | Johnson Controls Technology Company | Cooling strategy for battery systems |
| CN109962295A (en) * | 2017-12-14 | 2019-07-02 | 宝山钢铁股份有限公司 | A kind of temperature-control pressure-control formula formation of Li-ion batteries method |
| CN110148336A (en) * | 2019-03-27 | 2019-08-20 | 北京航空航天大学 | A kind of lithium ion battery charging process failure simulation method and device |
| CN110635187A (en) * | 2019-09-01 | 2019-12-31 | 南京理工大学 | Lithium battery charging method considering aging |
| CN111208431A (en) * | 2020-01-07 | 2020-05-29 | 天津市捷威动力工业有限公司 | Lithium ion battery all-weather calendar life prediction method for electric automobile |
| EP4046859A1 (en) * | 2021-02-19 | 2022-08-24 | Transportation IP Holdings, LLC | Battery management system |
| CN113178643A (en) * | 2021-03-16 | 2021-07-27 | 中国科学院电工研究所 | Hybrid heating method, system and equipment for low-temperature direct-current discharge of lithium ion battery |
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
| CN114572058A (en) * | 2022-03-09 | 2022-06-03 | 重庆金康赛力斯新能源汽车设计院有限公司 | Method and device for controlling temperature of battery system and electronic equipment |
| CN114725565A (en) * | 2022-03-29 | 2022-07-08 | 重庆金康赛力斯新能源汽车设计院有限公司 | High-temperature quick-charging cooling method, device, equipment and storage medium for lithium ion battery |
Non-Patent Citations (1)
| Title |
|---|
| 基于BMS的锂离子电池建模方法综述;梁新成等;《储能科学与技术》;20201130;第09卷(第06期);第1933-1939页 * |
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